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Special Issue "Recent Advances in Continuous Glucose Monitoring Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Martina Vettoretti
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, 35131, Padova PD, Italy
Interests: Signal processing and modeling techniques for the analysis of glucose sensor data; strategies for type 1 diabetes insulin therapy optimization; statistical learning; machine-learning techniques applied to clinical predictive model development
Special Issues and Collections in MDPI journals
Dr. Andrea Facchinetti
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Padova, Italy
Interests: Signal processing and classification of biomedical signals; algorithms and software to improve both performance and usability of continuous glucose monitoring (CGM) sensors; statistical methods and machine learning techniques to analyze big data in medicine
Prof. Dr. Giovanni Sparacino
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Padova, Italy
Interests: Sensors and algorithms for continuous glucose monitoring; deconvolution and parameter estimation techniques for the study of physiological systems; linear and nonlinear biological time-series analysis; measurement and processing of biomedical signals (EEG, event-related potentials, local field potentials, fNIRS, etc.) for clinical research and applications
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the last 20 years, we have experienced a revolution of glucose monitoring with the introduction of continuous glucose monitoring (CGM) sensors that can measure interstitial glucose concentration almost continuously for several days or weeks. CGM sensors can really be a game changer in the therapy of diabetes (especially type 1 diabetes), because the rich information they provide can be used to improve both patient and clinician decision-making with positive effects on glycemic control.

Most CGM devices currently on the market are based on minimally invasive electrochemical sensors. Implantable fluorescence sensors have also recently been developed and brought into the market. Other technologies have been investigated for noninvasive monitoring of glucose concentration in various biological fluids (e.g., interstitial fluid, tears, and saliva).

Although the first CGM sensors suffered from poor accuracy and required frequent calibration with capillary glucose measurements, great improvements were recently achieved for both sensing technologies and processing algorithms, with resulting improvements in sensor accuracy. Nevertheless, the accuracy of CGM sensors can still be problematic in some situations, such as immediately after sensor insertion, in proximity of the sensor end-of-life, and during rapid glucose changes. Moreover, the estimation of glucose trends and the generation of predictive alerts remains challenging because of the presence of noise on the CGM trace.

In this Special Issue, we seek original papers and review papers on the recent advances in CGM sensors, including:

  • New CGM technologies (e.g., new sensing technologies);
  • New algorithms for improving the analytical performance of CGM sensors (e.g., calibration and filtering algorithms);
  • New algorithms to enhance the output of CGM devices (e.g., new algorithms for trend estimation and alert generation).

Dr. Martina Vettoretti
Dr. Andrea Facchinetti
Prof. Dr. Giovanni Sparacino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • continuous glucose monitoring
  • wearable glucose sensors
  • minimally invasive glucose sensors
  • implantable glucose sensors
  • noninvasive glucose sensors
  • calibration algorithms
  • denoising
  • glucose prediction

Published Papers (2 papers)

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Research

Open AccessArticle
Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework
Sensors 2021, 21(9), 3188; https://doi.org/10.3390/s21093188 - 04 May 2021
Viewed by 256
Abstract
Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. [...] Read more.
Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient’s variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided—a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability. Full article
(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)
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Open AccessArticle
Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only
Sensors 2021, 21(5), 1647; https://doi.org/10.3390/s21051647 - 27 Feb 2021
Viewed by 597
Abstract
In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input [...] Read more.
In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different experimental protocols, making a comparison of their relative merits difficult. The aim of the present work was to perform a head-to-head comparison of thirty different linear and nonlinear predictive algorithms using the same dataset, given by 124 CGM traces collected over 10 days with the newest Dexcom G6 sensor available on the market and considering a 30-min prediction horizon. We considered the state-of-the art methods, investigating, in particular, linear black-box methods (autoregressive; autoregressive moving-average; and autoregressive integrated moving-average, ARIMA) and nonlinear machine-learning methods (support vector regression, SVR; regression random forest; feed-forward neural network, fNN; and long short-term memory neural network). For each method, the prediction accuracy and hypoglycemia detection capabilities were assessed using either population or individualized model parameters. As far as prediction accuracy is concerned, the results show that the best linear algorithm (individualized ARIMA) provides accuracy comparable to that of the best nonlinear algorithm (individualized fNN), with root mean square errors of 22.15 and 21.52 mg/dL, respectively. As far as hypoglycemia detection is concerned, the best linear algorithm (individualized ARIMA) provided precision = 64%, recall = 82%, and one false alarm/day, comparable to the best nonlinear technique (population SVR): precision = 63%, recall = 69%, and 0.5 false alarms/day. In general, the head-to-head comparison of the thirty algorithms fed by CGM data only made using a wide dataset shows that individualized linear models are more effective than population ones, while no significant advantages seem to emerge when employing nonlinear methodologies. Full article
(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)
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